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Best Python MCQ for Intermediate Developers Certification
Which clustering method requires setting a distance limit and a minimum number of data points to form a group?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Mean Shift
Option d – Mean Shift
Which of the following statements about DBSCAN is incorrect?
a. It can identify clusters with different shapes and sizes
b. It is affected by the sequence of data points
c. It can detect outliers and noisy data
d. It performs poorly with data that has many dimensions
Option b – It is affected by the sequence of data points
What is a major drawback of using hierarchical clustering on large-scale data?
a. It needs the number of clusters to be set in advance
b. It consumes a lot of computational resources
c. It struggles with complex, non-linear data separation
d. It is based on numerical distance calculations between data points
Option b – It consumes a lot of computational resources
Which clustering method relies on both data point connectivity and density?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Mean Shift
Option c – DBSCAN
In hierarchical clustering, which parameter controls how clusters are merged?
a. Distance metric
b. Number of clusters
c. Linkage criterion
d. Number of iterations
Option c – Linkage criterion
What is the most commonly used plot to represent clusters visually?
a. Scatter plot
b. Line plot
c. Bar chart
d. Pie chart
Option a – Scatter plot
How does the Mean Shift algorithm decide how many clusters to create?
a. By repeatedly estimating the bandwidth value
b. By merging nearby clusters based on data density
c. By reducing the total squared distance within clusters
d. By optimizing the silhouette score
Option a – By repeatedly estimating the bandwidth value
Which clustering technique is capable of managing clusters of different sizes and densities?
a. K-means clustering
b. Agglomerative hierarchical clustering
c. DBSCAN
d. Spectral clustering
Option c – DBSCAN
Which of the following is categorized as a density-based clustering algorithm?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Mean Shift
Option c – DBSCAN
Which clustering algorithm operates without needing the number of clusters specified ahead of time?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Spectral clustering
Option c – DBSCAN
In hierarchical clustering, what is the function of the linkage parameter?
a. To determine which distance measurement method is used
b. To choose how many clusters should be formed
c. To define the rule for combining clusters
d. To limit the depth of the dendrogram
Option c – To define the rule for combining clusters
Which algorithm is most appropriate for grouping text-based data?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Latent Dirichlet Allocation (LDA)
Option d – Latent Dirichlet Allocation (LDA)
In what way does K-means++ enhance the efficiency and accuracy of the K-means algorithm?
a. By choosing initial centroids that are near actual data points
b. By making sure each centroid runs for the same number of iterations
c. By omitting the initialization step to conserve memory
d. By lowering the total number of clusters created
Option a – By choosing initial centroids that are near actual data points
Which clustering method identifies cluster centers using a density-based approach?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Affinity propagation
Option d – Affinity propagation
Which clustering method is best suited for datasets with categorical variables?
a. K-means clustering
b. Agglomerative hierarchical clustering
c. DBSCAN
d. K-modes clustering
Option d – K-modes clustering
What sets Ward’s method apart from other linkage techniques in hierarchical clustering?
a. It increases the sum of squared distances within clusters
b. It reduces the sum of squared distances within clusters
c. It lowers the squared distances between clusters
d. It increases the squared distances between clusters
Option b – It reduces the sum of squared distances within clusters
Which clustering technique utilizes the Bayesian Information Criterion (BIC) to determine the best model?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Gaussian Mixture Models (GMM)
Option d – Gaussian Mixture Models (GMM)
In DBSCAN, which parameter is used to classify a data point as a core point, border point, or noise?
a. Epsilon
b. Min_samples
c. Metric
d. Leaf_size
Option b – Min_samples
Which clustering method aims to reduce internal cluster distances and enlarge the separation between different clusters?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Agglomerative hierarchical clustering
Option a – K-means clustering
Which Python-based algorithm is typically used to perform hierarchical clustering?
a. K-means clustering
b. DBSCAN
c. Hierarchical clustering
d. Agglomerative clustering
Option d – Agglomerative clustering
What is the primary goal of hierarchical clustering?
a. To assign data into a specific number of clusters
b. To determine the best number of clusters automatically
c. To organize clusters into a nested structure
d. To choose important features from the dataset
Option c – To organize clusters into a nested structure
In hierarchical clustering, which technique helps in computing the distance between two groups?
a. Single linkage
b. Complete linkage
c. Average linkage
d. Ward’s method
Option c – Average linkage
Which Python module is commonly used to apply hierarchical clustering?
a. NumPy
b. SciPy
c. scikit-learn
d. Pandas
Option b – SciPy
Is hierarchical clustering limited to datasets with only numerical values?
a. True
b. False
Option b – False
Which metric is frequently used to assess how similar two clusters are in hierarchical clustering?
a. Mutual information
b. Rand index
c. F-measure
d. Jaccard coefficient
Option b – Rand index
What kind of distance matrix is typically needed as input for hierarchical clustering?
a. Similarity matrix
b. Covariance matrix
c. Euclidean distance matrix
d. Correlation matrix
Option c – Euclidean distance matrix
What is a significant benefit of hierarchical clustering compared to other clustering techniques?
a. Uses fewer computational resources
b. Does not need the number of clusters to be set in advance
c. Offers higher accuracy in clustering results
d. Efficiently handles categorical data
Option b – Does not need the number of clusters to be set in advance
Which Python package is most often used to perform density-based clustering?
a. Pandas
b. NumPy
c. SciPy
d. Matplotlib
Option c – SciPy
What is a key strength of density-based clustering methods over other clustering techniques?
a. They can detect clusters of irregular shapes
b. They run faster in computation
c. They work without requiring data to be preprocessed
d. They avoid using distance metrics
Option a – They can detect clusters of irregular shapes
Which of the following is categorized as a density-based clustering method?
a. K-means clustering
b. Hierarchical clustering
c. DBSCAN
d. Gaussian Mixture Models
Option c – DBSCAN
What is the full form of DBSCAN?
a. Density-Based Spatial Clustering of Applications with Noise
b. Distance-Based Spatial Clustering of Applications with Noise
c. Distance-Based Silhouette Clustering of Applications with Noise
d. Density-Based Silhouette Clustering of Applications with Noise
Option a – Density-Based Spatial Clustering of Applications with Noise
Which two parameters are essential for configuring the DBSCAN algorithm?
a. MinPts and Eps
b. K and Eps
c. MinPts and K
d. MinPts and K-neighbors
Option a – MinPts and Eps
In DBSCAN, what does the Eps value indicate?
a. The smallest number of points needed to create a cluster
b. The largest allowable distance between two points to be considered neighbors
c. The upper limit on how many clusters can be formed
d. The maximum number of allowed iterations
Option b – The largest allowable distance between two points to be considered neighbors
Which of the following accurately describes a feature of DBSCAN?
a. It assigns each point to a definite cluster
b. It gives each point a density score based on neighboring points
c. It works only with numeric input
d. It cannot manage outliers or noise in the dataset
Option b – It gives each point a density score based on neighboring points
According to DBSCAN, which points are categorized as noise or outliers?
a. Points located in dense regions
b. Points situated in low-density areas
c. Points with negative density scores
d. Points that have zero density
Option b – Points situated in low-density areas
In DBSCAN, which step is used to identify outliers or noise within the dataset?
a. Checking for density-reachability
b. Searching for density-connected components
c. Assigning data to clusters
d. Not applicable
Option c – Assigning data to clusters
Which Python library is widely used for evaluating machine learning models?
a. sklearn
b. numPy
c. pandas
d. matplotlib
Option a – sklearn
Which technique is commonly used to assess how accurately a classification model performs?
a. Mean Absolute Error (MAE)
b. Mean Squared Error (MSE)
c. Confusion Matrix
d. R-squared
Option c – Confusion Matrix
What scoring method is generally applied to determine the effectiveness of clustering algorithms?
a. Silhouette score
b. Accuracy score
c. F1 score
d. Log loss
Option a – Silhouette score
Which metric can be utilized to evaluate the prediction quality of a recommender system?
a. Mean Absolute Error (MAE)
b. Root Mean Squared Error (RMSE)
c. Precision
d. R-squared
Option b – Root Mean Squared Error (RMSE)
What does recall indicate in the context of evaluating classification models?
a. The model’s capability to correctly predict the positive cases
b. The model’s capability to correctly predict the negative cases
c. The model’s accuracy in identifying both positive and negative instances
d. The model’s skill in assigning each sample to a class
Option a – The model’s capability to correctly predict the positive cases
Which metric is suitable for evaluating models that classify multiple categories?
a. Mean Absolute Error (MAE)
b. F1 Score
c. Root Mean Squared Error (RMSE)
d. R-squared
Option b – F1 Score
Which evaluation method is best suited for classification problems involving unbalanced class distributions?
a. Mean Absolute Error (MAE)
b. ROC Curve
c. Log Loss
d. RMSE
Option b – ROC Curve
Which metric is most appropriate for assessing the accuracy of a regression model that predicts numerical values?
a. F1 Score
b. R-squared
c. Confusion Matrix
d. Precision
Option b – R-squared
What method is commonly employed to evaluate the effectiveness of NLP models?
a. Mean Absolute Error (MAE)
b. F1 Score
c. Accuracy Score
d. BLEU Score
Option d – BLEU Score
What metric is best for assessing how well an anomaly detection model identifies rare events?
a. Mean Absolute Error (MAE)
b. Mean Squared Error (MSE)
c. ROC Curve
d. Precision-Recall Curve
Option d – Precision-Recall Curve
Which metric evaluates the proportion of variance in the target variable explained by a regression model?
a. F1 Score
b. R-squared
c. Confusion Matrix
d. Mean Absolute Error (MAE)
Option b – R-squared
What does a recommender system do?
a. It offers item suggestions to users based on their interests
b. It forecasts upcoming trends by analyzing past data
c. It studies user interactions to enhance website functionality
d. It shows ads according to the user’s browsing behavior
Option a – It offers item suggestions to users based on their interests
What are the primary categories of recommender systems?
a. Content-based filtering and collaborative filtering
b. Collaborative filtering and hybrid methods
c. Collaborative filtering and knowledge-driven systems
d. Hybrid methods and knowledge-based systems
Option a – Content-based filtering and collaborative filtering
Which recommender system type utilizes both item attributes and user preferences to generate suggestions?
a. Content-based filtering
b. Collaborative filtering
c. Hybrid filtering
d. Demographic filtering
Option a – Content-based filtering
Which algorithm is commonly associated with collaborative filtering approaches?
a. K-means clustering
b. Decision tree
c. Singular Value Decomposition (SVD)
d. Naive Bayes classifier
Option c – Singular Value Decomposition (SVD)
How are recommendations generated in collaborative filtering?
a. By identifying users with similar behavior and suggesting their preferred items
b. By analyzing product features to suggest related items
c. By looking at general preferences to recommend trending items
d. By using user profiles to provide customized suggestions
Option a – By identifying users with similar behavior and suggesting their preferred items
What is a known disadvantage of content-based filtering?
a. It doesn’t incorporate feedback or preferences from other users
b. It demands significant processing power
c. It favors frequently used items
d. It’s not easy to implement using Python
Option a – It doesn’t incorporate feedback or preferences from other users
What is a limitation of collaborative filtering techniques?
a. They consume a large amount of computational power
b. They struggle with datasets containing many missing values
c. They prioritize items with high popularity
d. They pose implementation challenges in Python
Option b – They struggle with datasets containing many missing values
Which approach merges content-based and collaborative filtering strengths?
a. Knowledge-based filtering
b. Hybrid filtering
c. Demographic filtering
d. Association rule learning
Option b – Hybrid filtering
Which Python package is suitable for creating recommendation engines?
a. Pandas
b. NumPy
c. SciPy
d. All of the above
Option d – All of the above
Which library is frequently utilized in Python for performing matrix factorization in recommender systems?
a. Pandas
b. NumPy
c. SciPy
d. Surprise
Option d – Surprise
Why is matrix factorization used in collaborative filtering?
a. To find related items for use in content-based systems
b. To group users with similar interests
c. To forecast user ratings for different items
d. To assess item similarity for hybrid recommendations
Option c – To forecast user ratings for different items
In what scenario is content-based filtering best utilized in recommendation systems?
a. When there is no data available about user preferences
b. When the goal is to provide tailored recommendations
c. When you aim to suggest items that are alike to what the user previously liked
d. When recommendations are based on demographic profiles
Option c – When you aim to suggest items that are alike to what the user previously liked
When is collaborative filtering the most suitable approach in a recommender system?
a. When user preference information is not accessible
b. When there’s a need to personalize user suggestions
c. When the aim is to recommend similar products
d. When suggestions rely on demographic characteristics
Option b – When there’s a need to personalize user suggestions
What is the main objective of item-based collaborative filtering?
a. To offer items similar to those a user previously engaged with
b. To provide suggestions using demographic information
c. To predict ratings that users may assign to products
d. To detect users with similar behavior or interests
Option a – To offer items similar to those a user previously engaged with
What is a common disadvantage of item-based collaborative filtering?
a. It requires significant computational effort
b. It mostly recommends widely popular products
c. It has difficulty dealing with incomplete or sparse data
d. It is often limited by the sparsity of the dataset
Option d – It is often limited by the sparsity of the dataset
What function does non-negative matrix factorization serve in collaborative filtering?
a. It helps estimate how users would rate different items
b. It aids in finding similar items for content-based systems
c. It is used to address sparsity problems in user-item data
d. It recommends widely preferred items across users
Option a – It helps estimate how users would rate different items
What is the purpose of item-item collaborative filtering?
a. To suggest items that resemble those previously liked by the user
b. To make recommendations based on demographic profiles
c. To calculate likely user ratings for items
d. To match users with similar interests
Option a – To suggest items that resemble those previously liked by the user
Why is cosine similarity useful in collaborative filtering?
a. To compute similarity scores between items in content-based systems
b. To evaluate how similar different users are in collaborative filtering
c. To project future user-item ratings
d. To suggest items with broad appeal across the user base
Option b – To evaluate how similar different users are in collaborative filtering
What is the function of TF-IDF in content-based recommendation systems?
a. To assess how similar two items are based on text
b. To determine how frequently an item is chosen
c. To predict how likely a user is to rate an item
d. To generate recommendations based on popularity
Option a – To assess how similar two items are based on text
What is a notable advantage of collaborative filtering compared to content-based filtering?
a. It does not rely on existing user preferences
b. It provides highly tailored recommendations
c. It is better at dealing with new-user scenarios
d. It has simpler implementation in code
Option b – It provides highly tailored recommendations
What is a key strength of content-based filtering over collaborative filtering?
a. It can operate without needing user preference data
b. It offers highly customized item suggestions
c. It handles the cold start problem more effectively
d. It’s generally easier to develop and deploy
Option c – It handles the cold start problem more effectively
What is the main purpose of user-based collaborative filtering?
a. To recommend products similar to the ones already liked
b. To use demographic information for item suggestions
c. To predict how a user would rate certain items
d. To identify and use preferences of users with similar interests
Option d – To identify and use preferences of users with similar interests
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